{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://tpq.io' target='_blank'>http://tpq.io</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:pff@tpq.io'>pff@tpq.io</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: red;\">**THE FOLLOWING PRESENTS CODES THAT ARE MOSTLY DIFFERENT FROM THE ORIGINAL CODE IN THE CHAPTER.**</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Web Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('ggplot')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ftplib"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "ed06650f-c346-4211-a504-918431567c00"
   },
   "source": [
    "import ftplib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "f9533953-66c8-428e-8476-fb9b5320cbc3"
   },
   "source": [
    "ftp = ftplib.FTP('YOUR_SERVER_DOMAIN.com')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "72343084-5e71-42bc-9649-acedaf9bf9b9"
   },
   "source": [
    "ftp.login(user='REPLACE', passwd='REPLACE')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "bb8b9a5f-2482-4c21-bb52-c5f0c659121b"
   },
   "source": [
    "np.save('./data/array', np.random.standard_normal((100, 100)))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "1e23d513-1cbc-4dff-87c8-35bbefd552ea"
   },
   "source": [
    "f = open('./data/array.npy', 'r')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "d7723e2d-8a38-4e76-b18d-47e804bbb9e5"
   },
   "source": [
    "ftp.storbinary('STOR array.npy', f)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "2827d953-cfad-44d3-a2bf-67cf5863a1f7"
   },
   "source": [
    "ftp.retrlines('LIST')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "90b28799-36f2-4591-8767-d01b85322596"
   },
   "source": [
    "f = open('./data/array_ftp.npy', 'wb').write"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "bda461e0-6d17-4011-8d12-cae2658b0938"
   },
   "source": [
    "ftp.retrbinary('RETR array.npy', f)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "fd643a77-53a7-4b88-80a1-992d37141e1b"
   },
   "source": [
    "ftp.delete('array.npy')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "b248ab34-3f84-434b-a4cf-75d3bef1f8fe"
   },
   "source": [
    "ftp.retrlines('LIST')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "372f7d6f-928a-436c-bf64-13b0aa994620"
   },
   "source": [
    "ftp.close()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "a1d9022c-bd30-43e1-b3c7-95bfefe6f60e"
   },
   "source": [
    "!ls -n ./data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "ed6eb7c9-0004-42a7-b0b3-0e1aa5f27ae1"
   },
   "source": [
    "!rm -f ./data/arr*\n",
    "  # clean-up directory"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "f51de772-ad07-4983-8914-d60383fc0393"
   },
   "source": [
    "ftps = ftplib.FTP_TLS('YOUR_SERVER_DOMAIN.com')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "442158b0-2c8a-47f5-a60d-3019c687b196"
   },
   "source": [
    "ftps.login(user='REPLACE', passwd='REPLACE')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "a8ab8f48-5524-423c-936f-b045ac5dc3b0"
   },
   "source": [
    "ftps.prot_p()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "80d163ef-151d-4536-8e16-7fc0774db409"
   },
   "source": [
    "ftps.retrlines('LIST')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "6cb73378-f642-4627-89a9-f6f69f076d9a"
   },
   "source": [
    "ftps.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### httplib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "uuid": "0d79f4d1-5d93-4c9d-9d62-40dc4981a457"
   },
   "outputs": [],
   "source": [
    "import http\n",
    "import http.client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "uuid": "d665bdf3-fc62-46c0-8f7f-ff125f12ca51"
   },
   "outputs": [],
   "source": [
    "http = http.client.HTTPConnection('hilpisch.com')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "uuid": "9285e3ee-2035-4816-897f-4c9e2c7c8ef0"
   },
   "outputs": [],
   "source": [
    "http.request('GET', '/index.htm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "uuid": "18a7deaf-ba30-440f-88e7-fd6c90245661"
   },
   "outputs": [],
   "source": [
    "resp = http.getresponse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "ca638e56-6667-4eea-af63-c2be54c86b9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 'OK')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp.status, resp.reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "ea7a144c-81bc-4cab-9132-c70af19773d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'<!doctype html>\\n<html lang=\"en\">\\n\\n\\t<head>\\n\\t\\t<meta charset=\"utf-8\">\\n\\n\\t\\t<title>Dr. Yves J. Hilpisch \\xe2\\x80'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content = resp.read()\n",
    "content[:100]\n",
    "  # first 100 characters of the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "f8ca3902-3e86-4398-8aa7-e301a0706871"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2763"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = content.find(b' E ')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "4cbca133-e69f-4f1f-8378-de0a212a6fdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b' E contact [at] dyjh [dot] de'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content[index:index + 29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "e1d9bb7e-2429-4a3b-8ad2-ce8e20f398d1"
   },
   "outputs": [],
   "source": [
    "http.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "7a44c652-b62f-4974-b7be-c1c708c432d2"
   },
   "outputs": [],
   "source": [
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "eeeecf85-e182-44d1-96ca-dfa7c7b770e0"
   },
   "outputs": [],
   "source": [
    "url = 'http://hilpisch.com/tr_eikon_eod_data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "d0222eb9-e696-477e-9647-adf7296153ac"
   },
   "outputs": [],
   "source": [
    "connect = urllib.request.urlopen(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "afb60cb7-6822-438a-81fd-43cdaca9290c"
   },
   "outputs": [],
   "source": [
    "data = connect.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "uuid": "b97fa159-af2c-4d4c-bd07-3b26e4942f86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Date,AAPL.O,MSFT.O,INTC.O,AMZN.O,GS.N,SPY,.SPX,.VIX,EUR=,XAU=,GDX,GLD\\n2010-01-04,30.57282657,30.95,20.88,133.9,173.08,113.33,1132.99,20.04,1.4411,1120.0,47.71,109.8\\n2010-01-05,30.625683660000004,30.96,20.87,134.69,176.14,113.63,1136.52,19.35,1.4368,1118.65,48.17,109.7\\n2010-01-06,30.138541290000003,30.77,20.8,132.25,174.26,113.71,1137.14,19.16,1.4412,1138.5,49.34,111.51\\n2010-01-07,30.082827060000003,30.452,20.6,130.0,177.67,114.19,1141.69,19.06,1.4318,1131.9,49.1,110.82\\n2010-01-08,30.28282686,30.66,20.83,133.52,174.31,114.57,1144.98,18.13,1.4412,1136.1,49.84,111.37\\n2010-01-11,30.015684270000005,30.27,20.95,130.308,171.56,114.73,1146.98,17.55,1.4513,1152.6,50.17,112.85\\n2010-01-12,29.674256040000003,30.07,20.608,127.35,167.82,113.66,1136.22,18.25,1.4494,1127.3,48.35,110.49\\n2010-01-13,30.092827050000004,30.35,20.96,129.11,169.07,114.62,1145.68,17.85,1.451,1138.4,48.86,111.54\\n2010-01-14,29.918541510000004,30.96,21.48,127.35,168.53,114.93,1148.46,17.63,1.4502,1142.85,48.6,112.03\\n2010-01-15,2'\n"
     ]
    }
   ],
   "source": [
    "print(data[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/yves/Documents/Temp/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "uuid": "60b60a3a-2cce-4f97-aaa7-c042f2e60279"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/Users/yves/Documents/Temp/data/eod_data.csv',\n",
       " <http.client.HTTPMessage at 0x10735d278>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urllib.request.urlretrieve(url, path + 'eod_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "acf4d984-2ff8-4258-9a6c-87a86ee2f116"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Date,AAPL.O,MSFT.O,INTC.O,AMZN.O,GS.N,SPY,.SPX,.VIX,EUR=,XAU=,GDX,GLD\\n',\n",
       " '2010-01-04,30.57282657,30.95,20.88,133.9,173.08,113.33,1132.99,20.04,1.4411,1120.0,47.71,109.8\\n',\n",
       " '2010-01-05,30.625683660000004,30.96,20.87,134.69,176.14,113.63,1136.52,19.35,1.4368,1118.65,48.17,109.7\\n',\n",
       " '2010-01-06,30.138541290000003,30.77,20.8,132.25,174.26,113.71,1137.14,19.16,1.4412,1138.5,49.34,111.51\\n',\n",
       " '2010-01-07,30.082827060000003,30.452,20.6,130.0,177.67,114.19,1141.69,19.06,1.4318,1131.9,49.1,110.82\\n']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = open(path + 'eod_data.csv', 'r')\n",
    "csv.readlines()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "62d5e85b-1590-450c-9018-746e6872f1fa"
   },
   "outputs": [],
   "source": [
    "!rm -f ./data/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Static Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "bb617800-1dc9-4cb9-a7b7-2b8cd16cf0b2"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "8e1e8943-d88d-4b61-bf19-15e55bebfb3a"
   },
   "outputs": [],
   "source": [
    "url = 'http://hilpisch.com/tr_eikon_eod_data.csv'\n",
    "data = pd.read_csv(url, parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "ca6562c0-9932-4d5c-9811-4512e717fac3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z9vRMjZ0Qu86mDf4SM5F5TdvtjNljX8zeB2JGjG7+OpGeqYfvTD6Wl+enVOg7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\nZEpERESkCEqmRERERIrw/07nsRSM2SSUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10737f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Date', y='MSFT.O', figsize=(10, 6));\n",
    "# tag: microsoft\n",
    "# title: Historical stock prices for Microsoft since January 2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**REMARK**: The original version used Bokeh for Web plotting. Plotly seems to be the more easy and intuitive way for generating interactive D3.js Web plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "e6364d22-c102-4a31-8bf1-35edbc5bbaae"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "text": "",
         "type": "scatter",
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47.81, 48.62, 48.46, 48.08, 48.84, 48.42, 47.695, 47.59, 46.9, 47.17, 46.95, 46.67, 45.16, 45.74, 47.52, 47.66, 47.98, 48.45, 48.14, 47.88, 47.45, 47.02, 46.45, 46.76, 46.325, 45.65, 46.23, 47.59, 47.19, 46.6, 46.355, 45.955, 45.48, 46.24, 46.39, 45.92, 47.13, 47.18, 47.01, 42.66, 41.19, 42.01, 40.4, 41.28, 41.6, 41.84, 42.45, 42.41, 42.36, 42.6, 42.38, 43.09, 43.87, 43.58, 43.53, 43.5, 43.855, 44.15, 44.09, 43.99, 44.055, 43.85, 43.88, 43.28, 43.055, 43.11, 42.36, 42.85, 42.03, 41.98, 41.02, 41.38, 41.56, 41.695, 42.5, 42.285, 42.88, 42.855, 42.9, 41.46, 41.21, 40.97, 40.96, 40.655, 40.72, 40.29, 41.545, 41.53, 41.42, 41.48, 41.72, 41.76, 41.65, 42.255, 42.16, 41.615, 42.905, 42.635, 42.985, 43.34, 47.87, 48.03, 49.155, 49.06, 48.64, 48.655, 48.24, 47.6, 46.28, 46.7, 47.75, 47.37, 47.35, 47.625, 48.72, 48.295, 48.01, 47.58, 47.58, 47.42, 46.9, 46.59, 47.61, 47.45, 46.86, 47.23, 46.92, 46.85, 46.36, 46.14, 45.73, 45.65, 46.61, 46.44, 45.97, 45.475, 45.83, 45.97, 46.72, 46.1, 46.23, 45.91, 45.635, 45.65, 45.26, 44.37, 44.15, 44.445, 44.4, 44.39, 44.3, 44.24, 44.52, 44.61, 45.54, 45.62, 45.76, 46.66, 46.62, 46.92, 47.28, 45.54, 46.11, 45.94, 45.35, 45.34, 46.29, 46.88, 46.7, 46.81, 47.54, 47.58, 46.62, 46.74, 47.33, 46.41, 46.74, 46.73, 47.0, 47.32, 47.27, 46.61, 45.66, 43.07, 41.68, 40.47, 42.71, 43.9, 43.93, 43.52, 41.82, 43.36, 43.5, 42.61, 43.89, 43.07, 43.29, 43.48, 43.04, 43.98, 44.3, 44.25, 43.48, 44.11, 43.9, 43.87, 43.91, 43.94, 43.29, 43.44, 44.26, 44.61, 45.57, 46.63, 46.75, 46.8, 47.45, 47.11, 47.0, 46.89, 46.68, 47.01, 47.51, 47.62, 47.77, 47.2, 48.03, 52.87, 54.25, 53.69, 53.98, 53.36, 52.64, 53.24, 54.15, 54.4, 54.38, 54.92, 54.16, 53.51, 53.65, 53.32, 52.84, 53.765, 52.97, 53.85, 53.94, 54.19, 54.19, 54.25, 53.69, 53.93, 54.35, 55.22, 55.21, 54.2, 55.91, 55.81, 55.79, 54.98, 55.27, 54.06, 55.14, 55.2, 56.13, 55.7, 54.13, 54.83, 55.35, 55.82, 55.67, 55.95, 56.55, 56.31, 55.48, 54.8, 55.05, 54.05, 52.17, 52.33, 52.3, 52.78, 51.64, 53.11, 50.99, 50.56, 50.79, 50.48, 52.29, 51.79, 52.17, 51.22, 52.055, 55.09, 54.71, 53.0, 52.16, 52.0, 50.16, 49.41, 49.28, 49.71, 49.69, 50.5, 51.09, 52.42, 52.19, 51.82, 52.65, 51.18, 51.36, 52.1, 51.3, 50.88, 52.58, 52.95, 52.35, 52.03, 51.03, 51.65, 52.84, 52.05, 53.07, 53.17, 53.59, 54.35, 54.66, 53.49, 53.86, 54.07, 53.97, 54.21, 53.54, 54.71, 55.05, 55.23, 55.57, 55.43, 54.56, 55.12, 54.46, 54.42, 54.31, 54.65, 55.35, 55.36, 55.65, 56.46, 56.39, 55.59, 55.78, 51.78, 52.11, 51.44, 50.94, 49.9, 49.87, 50.61, 49.78, 49.87, 49.94, 50.39, 50.07, 51.02, 51.05, 51.51, 51.08, 51.83, 50.51, 50.81, 50.32, 50.62, 50.03, 51.59, 52.12, 51.89, 52.32, 53.0, 52.85, 52.48, 51.79, 52.13, 52.1, 52.04, 51.62, 51.48, 50.14, 49.83, 49.69, 50.39, 50.13, 50.07, 51.19, 50.99, 51.91, 49.83, 48.43, 49.44, 50.54, 51.17, 51.16, 51.17, 51.38, 51.38, 52.3, 52.59, 53.21, 53.51, 53.74, 53.7, 53.96, 53.09, 55.91, 55.8, 56.57, 56.73, 56.76, 56.19, 56.21, 56.68, 56.58, 56.58, 56.97, 57.39, 57.96, 58.06, 58.2, 58.02, 58.3, 57.94, 58.12, 57.44, 57.56, 57.6, 57.62, 57.67, 57.89, 57.95, 58.17, 58.03, 58.1, 57.89, 57.46, 57.59, 57.67, 57.61, 57.66, 57.43, 56.21, 57.05, 56.53, 56.26, 57.19, 57.25, 56.93, 56.81, 57.76, 57.82, 57.43, 56.9, 57.95, 58.03, 57.4, 57.6, 57.42, 57.24, 57.64, 57.74, 57.8, 58.04, 57.19, 57.11, 56.92, 57.42, 57.22, 57.66, 57.53, 57.25, 59.66, 61.0, 60.99, 60.63, 60.1, 59.87, 59.92, 59.8, 59.43, 59.21, 58.71, 60.42, 60.47, 60.17, 58.7, 59.02, 58.12, 58.87, 59.65, 60.64, 60.35, 60.86, 61.12, 60.4, 60.53, 60.61, 61.09, 60.26, 59.2, 59.25, 60.22, 59.95, 61.37, 61.01, 61.97, 62.17, 62.98, 62.68, 62.58, 62.3, 63.62, 63.54, 63.54, 63.55, 63.24, 63.28, 62.99, 62.9, 62.14, 62.58, 62.3, 62.3, 62.84, 62.64, 62.62, 63.19, 62.61, 62.7, 62.53, 62.5, 62.3, 62.74, 62.96, 63.52, 63.68, 64.27, 65.78, 65.13, 64.65, 63.58, 63.17, 63.68, 63.64, 63.43, 63.34, 64.06, 64.0, 64.72, 64.57, 64.53, 64.52, 64.62, 64.49, 64.36, 64.62, 64.62, 64.23, 63.98, 64.94, 64.01, 64.25, 64.27, 64.4, 64.99, 64.73, 64.93, 64.71, 64.41, 64.75, 64.64, 64.87, 64.93, 64.21, 65.03, 64.87, 64.98, 65.1, 65.29, 65.47, 65.71, 65.86, 65.55, 65.73, 65.56, 65.73, 65.68, 65.53, 65.48, 65.23, 64.95, 65.48, 65.39, 65.04, 65.5, 66.4, 67.53, 67.92, 67.83, 68.27, 68.46, 69.41, 69.3, 69.08, 68.81, 69.0, 68.94, 69.04, 69.31, 68.46, 68.38, 68.43, 69.41, 67.48, 67.71, 67.69, 68.45, 68.68, 68.77, 69.62, 69.96, 70.41, 69.84, 70.1, 71.76, 72.28, 72.52, 72.39, 71.95, 70.32, 69.78, 70.65, 70.27, 69.9, 70.0, 70.87, 69.91, 70.27, 70.26, 71.21, 70.53, 69.21, 69.8, 68.49, 68.93, 68.17, 69.08, 68.57, 69.46, 69.98, 69.99, 71.15, 71.77, 72.78, 73.35, 73.3, 73.86, 74.22, 73.79, 73.6, 74.19, 74.05, 73.16, 73.04, 72.7, 72.58, 72.26, 72.15, 72.68, 72.4, 72.79, 72.47, 71.41, 72.5, 73.59, 73.22, 73.65, 72.4, 72.49, 72.15, 73.16, 72.72, 72.69, 72.82, 72.83, 73.05, 74.01, 74.77, 73.94, 73.61, 73.4, 74.34, 73.98, 74.76, 74.68, 75.21, 74.77, 75.31, 75.16, 75.44, 74.94, 74.21, 74.41, 73.26, 73.26, 73.85, 73.87, 74.49, 74.61, 74.26, 74.69, 75.97, 76.0, 76.29, 76.29, 76.42, 77.12, 77.49, 77.65, 77.59, 77.61, 77.91, 78.81, 78.83, 78.86, 78.63, 78.76, 83.81, 83.89, 83.18], \"name\": \"MSFT.O\", \"text\": \"\", \"line\": {\"color\": \"rgba(255, 153, 51, 1.0)\", \"width\": 1.3, \"dash\": \"solid\"}, \"mode\": \"lines\"}], {\"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}}, \"paper_bgcolor\": \"#F5F6F9\", \"plot_bgcolor\": \"#F5F6F9\", \"yaxis1\": {\"tickfont\": {\"color\": \"#4D5663\"}, \"gridcolor\": \"#E1E5ED\", \"titlefont\": {\"color\": \"#4D5663\"}, \"zerolinecolor\": \"#E1E5ED\", \"showgrid\": true, \"title\": \"\"}, \"xaxis1\": {\"tickfont\": {\"color\": \"#4D5663\"}, \"gridcolor\": \"#E1E5ED\", \"titlefont\": {\"color\": \"#4D5663\"}, \"zerolinecolor\": \"#E1E5ED\", \"showgrid\": true, \"title\": \"\"}, \"titlefont\": {\"color\": \"#4D5663\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# direct plotting with Cufflinks within the notebook\n",
    "iplot(data.set_index('Date')['MSFT.O'].iplot(asFigure=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real-Time Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Real-Time FX Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Real-Time Stock Price Quotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rapid Web Applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traders' Chat Room"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Python Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports and Database Preliminaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Core Functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Templating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "uuid": "1463132b-34c8-4238-8d3e-6ba5c7562194"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1, 2, 3'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'%d, %d, %d' % (1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "uuid": "c5c11d6d-ad65-4ee7-9ebe-91dc1c3d2497"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1, 2, 3'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{}, {}, {}'.format(1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "uuid": "7ff70268-2c8f-45a5-a6b0-17bb9d77cc14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1, 2, 3'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{}, {}, {}'.format(*'123')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "uuid": "da70e7f6-9068-4039-bdfc-8bfe510a7ee6"
   },
   "outputs": [],
   "source": [
    "templ = '''<!doctype html>\n",
    "  Just print out <b>numbers</b> provided to the template.\n",
    "  <br><br>\n",
    "  {% for number in numbers %}\n",
    "    {{ number }}\n",
    "  {% endfor %}\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "uuid": "6b66b22b-fb7f-4dc7-99f4-ed6550e9eb2f"
   },
   "outputs": [],
   "source": [
    "from jinja2 import Template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "uuid": "641a5ee8-9ef7-4e60-80cb-c73b1d81f182"
   },
   "outputs": [],
   "source": [
    "t = Template(templ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "uuid": "ad91b975-9917-4d22-99db-635d745308a8"
   },
   "outputs": [],
   "source": [
    "html = t.render(numbers=range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "uuid": "3ddf0465-00f2-4c70-8547-7460eba6f11d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<!doctype html>\\n  Just print out <b>numbers</b> provided to the template.\\n  <br><br>\\n  \\n    0\\n  \\n    1\\n  \\n    2\\n  \\n    3\\n  \\n    4\\n  '"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "uuid": "d8d44498-a17f-41b3-9e91-6d09a288c5b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!doctype html>\n",
       "  Just print out <b>numbers</b> provided to the template.\n",
       "  <br><br>\n",
       "  \n",
       "    0\n",
       "  \n",
       "    1\n",
       "  \n",
       "    2\n",
       "  \n",
       "    3\n",
       "  \n",
       "    4\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Styling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "uuid": "41649870-1061-444f-924c-733ecdef2ebb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../python36/tradechat\n",
      "tables.sql\n",
      "tradechat.db\n",
      "tradechat.py\n",
      "../python36/tradechat/static\n",
      "style.css\n",
      "../python36/tradechat/templates\n",
      "layout.html\n",
      "login.html\n",
      "register.html\n",
      "show_entries.html\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for path, dirs, files in os.walk('../python36/tradechat'):\n",
    "  print(path)\n",
    "  for f in files:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Services"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Financial Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "uuid": "79f9048b-3427-4a79-9cbc-4bf414c0730c"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import requests\n",
    "sys.path.append('../python36/volservice')\n",
    "  # adjust if necessary to your path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "uuid": "baba8c94-1d56-42aa-a491-da6b7a9f649f"
   },
   "outputs": [],
   "source": [
    "from werkzeug.wrappers import Request, Response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "uuid": "5cbccdda-a5a3-4c7e-97ac-3df9b05c5efb"
   },
   "outputs": [],
   "source": [
    "from vol_pricing_service import get_option_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "uuid": "b6bad926-3093-4f80-aedd-aef6e7950749"
   },
   "outputs": [],
   "source": [
    "def application(environ, start_response):\n",
    "    request = Request(environ)\n",
    "      # wrap environ in new object\n",
    "    text = get_option_value(request.args)\n",
    "      # provide all paramters of the call to function\n",
    "      # get back either error message or option value\n",
    "    response = Response(text, mimetype='text/html')\n",
    "      # generate response object based on the returned text\n",
    "    return response(environ, start_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "uuid": "a5708e66-1b39-4f82-b07f-44cb07c68280"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "url = 'http://localhost:4000/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start the service on the shell in the appropriate folder:\n",
    "\n",
    "    $ python vol_pricing.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing parameter V0 (current volatility level)\n",
      "Missing parameter kappa (mean reversion factor)\n",
      "Missing parameter theta (long-run mean of volatility)\n",
      "Missing parameter sigma (volatility of volatility)\n",
      "Missing parameter zeta (factor of the expected volatility risk premium)\n",
      "Missing parameter T (time horizon in years)\n",
      "Missing parameter r (risk-free interest rate)\n",
      "Missing parameter K (strike)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(requests.get(url).text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "urlpara = url + 'application?V0=%s&kappa=%s&theta=%s&sigma=%s&zeta=%s'\n",
    "urlpara += '&T=%s&r=%s&K=%s'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://localhost:4000/application?V0=25&kappa=2.0&theta=20&sigma=1.0&zeta=0.0&T=1.5&r=0.02&K=22.5'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urlval = urlpara % (25, 2.0, 20, 1.0, 0.0, 1.5, 0.02, 22.5)\n",
    "urlval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.202937705934\n"
     ]
    }
   ],
   "source": [
    "print(requests.get(urlval).text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 196 ms, sys: 33.2 ms, total: 229 ms\n",
      "Wall time: 441 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "urlpara = 'http://localhost:4000/application?V0=25&kappa=2.0'\n",
    "urlpara += '&theta=25&sigma=1.0&zeta=0.0&T=1&r=0.02&K=%s'\n",
    "strikes = np.linspace(20, 30, 50)\n",
    "results = []\n",
    "for K in strikes:\n",
    "     results.append(float(requests.get(urlpara % K).text))\n",
    "results = np.array(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.91296701,  4.71661296,  4.52120153,  4.32692516,  4.1339945 ,\n",
       "        3.94264561,  3.75313813,  3.56575972,  3.38079846,  3.19858765,\n",
       "        3.01946028,  2.8437621 ,  2.67184576,  2.50406508,  2.34078693,\n",
       "        2.18230495,  2.02898213,  1.88111287,  1.738968  ,  1.60280064,\n",
       "        1.47281111,  1.34917004,  1.23204859,  1.12141092,  1.01739405,\n",
       "        0.9199686 ,  0.82907686,  0.74462353,  0.66647327,  0.59445387,\n",
       "        0.52843174,  0.46798166,  0.41300694,  0.36319553,  0.31824647,\n",
       "        0.27785656,  0.24171678,  0.20951651,  0.18094732,  0.1557064 ,\n",
       "        0.1334996 ,  0.11414975,  0.09710449,  0.08234678,  0.06958767,\n",
       "        0.05859317,  0.04915788,  0.04109348,  0.03422854,  0.02840802])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S2ckluXozxpgCFgIjY4wvAb3yG5Z6qmQSbjrgZmZzOIPef45Ee/uqHoQmWJKkcpBLcvVc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\nT//Ro6i+c26hQ5Q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VCleuJEkbZF3bhwD9+sGXvtROasgQyLDC9XxqGFOmVANQUZFi+PAO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\nRSOxsH5uOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109360240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(strikes, results, 'b')\n",
    "plt.plot(strikes, results, 'ro')\n",
    "plt.grid(True)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('European call option value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Listed Volatility and Variance Derivatives** |\n",
    "<a href=\"http://lvvd.tpq.io\" target=\"_blank\">Listed VV Derivatives @ Wiley Finance</a>\n",
    "\n",
    "**Python Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">Python for Finance University Certificate</a>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
